2 Preparing Raster Data

Code

# load_packages()

2.1 Load data

2.1.1 Sampling location data

Code

# Load soil data from sampling locations
bern_data <- readr::read_csv(
  here::here("data-raw/soildata/berne_soil_sampling_locations.csv")
  )

# Display data
head(bern_data) |> knitr::kable()

site_id_unique	timeset	x	y	dataset	dclass	waterlog.50	waterlog.100	ph.0.10	ph.10.30	ph.30.50	ph.50.100
4_26-In-005	d1968_1974_ptf	2571994	1203001	validation	poor	0	1	6.071733	6.227780	7.109235	7.214589
4_26-In-006	d1974_1978	2572149	1202965	calibration	poor	1	1	6.900000	6.947128	7.203502	7.700000
4_26-In-012	d1974_1978	2572937	1203693	calibration	moderate	1	1	6.200000	6.147128	5.603502	5.904355
4_26-In-014	d1974_1978	2573374	1203710	validation	well	0	0	6.600000	6.754607	7.200000	7.151129
4_26-In-015	d1968_1974_ptf	2573553	1203935	validation	moderate	0	1	6.272715	6.272715	6.718392	7.269008
4_26-In-016	d1968_1974_ptf	2573310	1204328	calibration	poor	0	1	6.272715	6.160700	5.559031	5.161655

The dataset on soil samples from Bern holds 13 variables for 1052 entries (more information here):

site_id_unique: The location’s unique site id.
timeset: The sampling year and information on sampling type for soil pH (no label: CaCl\(_2\) laboratory measurement, field: indicator solution used in field, ptf: H\(_2\)O laboratory measurement transferred by pedotransfer function).
x: The x (easting) coordinates in meters following the (CH1903/LV03) system.
y: The y (northing) coordinates in meters following the (CH1903/LV03) system.
dataset: Specification whether a sample is used for model calibration or validation (this is based on randomization to ensure even spatial coverage).
dclass: Soil drainage class
waterlog.30, waterlog.50, waterlog.100: Specification whether soil was water logged at 30, 50, or 100 cm depth (0 = No, 1 = Yes).
ph.0.10, ph.10.30, ph.30.50, ph.50.100: Average soil pH between 0-10, 10-30, 30-50, and 50-100 cm depth.

2.1.2 Raster data of covariates

Now, let’s load all the covariates that we want to produce our soil maps.

Code

# Get a list with the path to all raster files
list_raster <- 
  base::list.files(
    here::here("data-raw/geodata/covariates/"),
    full.names = T
  )

# Display data (lapply to clean names)
lapply(
  list_raster, 
  function(x) sub(".*/(.*)", "\\1", x)
  ) |> 
  unlist() |> 
  head(5) |> 
  print()

[1] "be_gwn25_hdist.tif" "be_gwn25_vdist.tif" "cindx10_25.tif"    
[4] "cindx50_25.tif"     "geo500h1id.tif"

The output above shows the first five raster files with rather cryptic names. For this tutorial, we do not need to know all of the 91 rasters files that we are loading here but in a scientific context, you should know your data better of course. Let’s look at one of these raster files to get a better feeling for our data:

Code

# Load a raster file as example: Picking the slope profile at 2m resolution
raster_example <- terra::rast(list_raster[74])
raster_example

class       : SpatRaster 
dimensions  : 986, 2428, 1  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : 2568140, 2616700, 1200740, 1220460  (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95 
source      : Se_slope2m.tif 
name        : Se_slope2m 
min value   :    0.00000 
max value   :   85.11286

As shown in the output, a raster object has the following properties (among others, see ?terra::rast):

class: The class of the file, here a SpatRaster.
dimensions: The number of rows, columns, years (if temporal encoding).
resolution: The resolution of the coordinate system, here it is 20 in both axes.
extent: The extent of the coordinate system defined by min and max values on the x and y axes.
coord. ref.: Reference coordinate system. Here, the raster is encoded using the LV95 geodetic reference system from which the projected coordinate system CH1903+ is derived.
source: The name of the source file.
names: The name of the raster file (mostly the file name without file-specific ending)
min value: The lowest value of all cells.
max value: The highest value of all cells.

Now, let’s look at a visualisation of this raster file. Since we selected the slope at 2m resolution, we expect a relief-like map with a color gradient that indicates the steepness of the terrain.

Code

# Plot raster example
terra::plot(raster_example)

Code

# To have some more flexibility, we can plot this in the ggplot-style as such:
ggplot2::ggplot() +
  tidyterra::geom_spatraster(data = raster_example) +
  ggplot2::scale_fill_viridis_c(
    na.value = NA,
    option = "magma",
    name = "Slope (%) \n"
    ) +
  ggplot2::theme_bw() +
  ggplot2::scale_x_continuous(expand = c(0, 0)) +  # avoid gap between plotting area and axis
  ggplot2::scale_y_continuous(expand = c(0, 0)) +
  ggplot2::labs(title = "Slope of the Study Area")

Note

Note that the second plot has different coordinates than the upper one. That is because the data was automatically projected to the World Geodetic System (WGS84, ESPG: 4326).

This looks already interesting but we can put our data into a bit more context. For example, a larger map background would be useful to get a better orientation of our location. Also, it would be nice to see where our sampling locations are and to differentiate these locations by whether they are part of the calibration or validation dataset. Bringing this all together requires some more understanding of plotting maps in R. So, don’t worry if you do not understand everything in the code chunk below and enjoy the visualisations:

Code

# To get our map working correctly, we have to ensure that all the input data
# is in the same coordinate system. Since our Berne data is in the Swiss 
# coordinate system, we have to transform the sampling locations to the 
# World Geodetic System first.

# For the raster:
r <- terra::project(raster_example, "+init=EPSG:4326")

# For the sampling locations:
# Function from Stackoverflow:
# https://stackoverflow.com/questions/49536664/r-transforming-coordinates-inside-the-data-frame
change_coords <-function(data, from_CRS, to_CRS) {
  
  # Load required package
  require(sp)
  
  # Turn string into CRS
  from_CRS = CRS(from_CRS)
  to_CRS   = CRS(to_CRS)
  
  new <- 
    as.data.frame(
      spTransform(
        SpatialPointsDataFrame(
          coords = data.frame(
            lon = data$lon,
            lat = data$lat),
          
          data = data.frame(
            id = data$id,
            lon_old = data$lon,
            lat_old = data$lat),
          proj4string = from_CRS), 
        to_CRS
        )
      )
  
  new <- 
    new |> 
    dplyr::select(coords.x1, coords.x2, id) |> 
    dplyr::rename(lon = coords.x1,
                  lat = coords.x2)
  
  return(new)
}

# Transform dataframes
coord_cal <- 
  bern_data |> 
  dplyr::filter(dataset == "calibration") |> 
  dplyr::select(site_id_unique, x, y) |> 
  dplyr::rename(id = site_id_unique, lon = x, lat = y) |> 
  change_coords(
    from_CRS = "+init=epsg:2056", 
    to_CRS = "+init=epsg:4326"
    )

coord_val <- 
  bern_data |> 
  dplyr::filter(dataset == "validation") |> 
  dplyr::select(site_id_unique, x, y) |> 
  dplyr::rename(id = site_id_unique, lon = x, lat = y) |> 
  change_coords(
    from_CRS = "+init=epsg:2056", 
    to_CRS = "+init=epsg:4326"
    )

Code

# Loading packages to improve code readbility to avoid :: notation
# Note: This code may only work when installing the development branch of {leaflet}:
# remotes::install_github('rstudio/leaflet')
library(leaflet)
library(terra)

# Let's get a nice color palette now for easy reference
pal <- colorNumeric(
  "magma",
  values(r),
  na.color = "transparent"
  )

# Next, we build a leaflet map
leaflet() |> 
  # As base maps, use two provided by ESRI
  addProviderTiles(providers$Esri.WorldImagery, group = "World Imagery") |>
  addProviderTiles(providers$Esri.WorldTopoMap, group = "World Topo") |>
  # Add our raster file
  addRasterImage(
    r,
    colors = pal,
    opacity = 0.6,
    group = "raster"
    ) |>
  # Add markers for sampling locations
  leaflet::addCircleMarkers(
    data = coord_cal,
    lng = ~lon,  # Column name for x coordinates
    lat = ~lat,  # Column name for y coordinates
    group = "training",
    color = "black"
  ) |>
    leaflet::addCircleMarkers(
    data = coord_val,
    lng = ~lon,  # Column name for x coordinates
    lat = ~lat,  # Column name for y coordinates
    group = "validation",
    color = "red"
  ) |>
  # Add some layout and legend
  addLayersControl(
    baseGroups = c("World Imagery","World Topo"),
    position = "topleft",
    options = layersControlOptions(collapsed = FALSE),
    overlayGroups = c("raster", "training", "validation")
    ) |>
  addLegend(
    pal = pal,
    values = values(r),
    title = "Slope (%)")

Note

This plotting example is based to the one shown in the AGDS 2 tutorial “Handful of Pixels” on phenology and more information on using spatial data in R can be found in the Chapter on Geospatial data in R.

That looks great! At first glance, it is a bit crowded but once you scroll in you can investigate our study area quite nicely. You can check whether the slope raster file makes sense by comparing it against the base maps. Can you see how cliffs along the Aare river, hills, and even gravel quarries show high slopes? We also see that our validation dataset is nicely distributed across the area covered by the training dataset.

Now that we have played with a few visualizations, let’s get back to preparing our data. The {terra} package comes with the very useful tool to stack multiple raster on top of each other, if they are of the same spatial format. To do so, we just have to feed in the vector of file names list_raster:

Code

# Load all files as one batch
all_rasters <- terra::rast(list_raster)
all_rasters

class       : SpatRaster 
dimensions  : 986, 2428, 91  (nrow, ncol, nlyr)
resolution  : 20, 20  (x, y)
extent      : 2568140, 2616700, 1200740, 1220460  (xmin, xmax, ymin, ymax)
coord. ref. : CH1903+ / LV95 
sources     : be_gwn25_hdist.tif  
              be_gwn25_vdist.tif  
              cindx10_25.tif  
              ... and 88 more source(s)
names       :  be_gw~hdist, be_gw~vdist, cindx10_25, cindx50_25, geo500h1id, geo500h3id, ... 
min values  :    0.4998779,       0.000,       -100,  -99.99999,          1,          0, ... 
max values  : 3181.2531475,    1165.709,        100,   57.94390,         99,         56, ...

Now, we do not want to have the covariates’ data from all cells in the raster file. Rather, we want to reduce our stacked rasters to the x and y coordinates for which we have soil sampling data. We can do this using the terra::extract() function. Then, we want to merge the two dataframes of soil data and covariates data by their coordinates. Since the order of the covariate data is the same as the Bern data, we can simply bind their columns with cbind():

Code

# Extract coordinates from sampling locations
sampling_xy <- bern_data |> dplyr::select(x, y)

# From all rasters, extract values for sampling coordinates
covar_data <- 
  terra::extract(all_rasters,  # The raster we want to extract from
                 sampling_xy,  # A matrix of x and y values to extract for
                 ID = FALSE    # To not add a default ID column to the output
                 )

merged_data <- cbind(bern_data, covar_data)
head(merged_data) |> knitr::kable()

site_id_unique	timeset	x	y	dataset	dclass	waterlog.50	waterlog.100	ph.0.10	ph.10.30	ph.30.50	ph.50.100	be_gwn25_hdist	be_gwn25_vdist	cindx10_25	cindx50_25	geo500h1id	lgm	lsf	mrrtf25	mrvbf25	mt_gh_y	mt_rr_y	mt_td_y	mt_tt_y	mt_ttvar	NegO	PosO	protindx	Se_alti2m_std_50c	Se_conv2m	Se_curv25m	Se_curv2m_fmean_50c	Se_curv2m_fmean_5c	Se_curv2m_s60	Se_curv2m_std_50c	Se_curv2m_std_5c	Se_curv2m	Se_curv50m	Se_curv6m	Se_curvplan25m	Se_curvplan2m_fmean_50c	Se_curvplan2m_fmean_5c	Se_curvplan2m_s60	Se_curvplan2m_s7	Se_curvplan2m_std_50c	Se_curvplan2m_std_5c	Se_curvplan2m	Se_curvplan50m	Se_curvprof25m	Se_curvprof2m_fmean_50c	Se_curvprof2m_fmean_5c	Se_curvprof2m_s60	Se_curvprof2m_s7	Se_curvprof2m_std_50c	Se_curvprof2m_std_5c	Se_curvprof2m	Se_curvprof50m	Se_diss2m_50c	Se_diss2m_5c	Se_e_aspect25m	Se_e_aspect2m_5c	Se_e_aspect2m	Se_e_aspect50m	Se_MRRTF2m	Se_MRVBF2m	Se_n_aspect2m_50c	Se_n_aspect2m_5c	Se_n_aspect2m	Se_n_aspect50m	Se_n_aspect6m	Se_NO2m_r500	Se_PO2m_r500	Se_rough2m_10c	Se_rough2m_5c	Se_rough2m_rect3c	Se_SAR2m	Se_SCA2m	Se_slope2m_fmean_50c	Se_slope2m_fmean_5c	Se_slope2m_s60	Se_slope2m_s7	Se_slope2m_std_50c	Se_slope2m_std_5c	Se_slope2m	Se_slope50m	Se_slope6m	Se_tpi_2m_50c	Se_tpi_2m_5c	Se_tri2m_altern_3c	Se_tsc10_2m	Se_TWI2m_s15	Se_TWI2m_s60	Se_TWI2m	Se_vrm2m_r10c	Se_vrm2m	terrTextur	tsc25_18	tsc25_40	vdcn25	vszone
4_26-In-005	d1968_1974_ptf	2571994	1203001	validation	poor	0	1	6.071733	6.227780	7.109235	7.214589	234.39087	1.2986320	-10.62191	-6.9658718	6	7	0.0770846	0.0184651	4.977099	1316.922	9931.120	58	98	183	1.569110	1.534734	0.0159717	0.3480562	-40.5395088	-0.0014441	-0.0062570	0.0175912	0.0002296	2.9204133	1.1769447	-1.9364884	0.0031319	-0.5886537	-0.0042508	-0.0445323	-0.0481024	-0.0504083	-0.1655090	1.5687343	0.6229440	-1.0857303	0.0007920	-0.0028067	-0.0382753	-0.0656936	-0.0506380	-0.0732220	1.6507173	0.7082230	0.8507581	-0.0023399	0.3934371	0.1770810	-0.9702092	-0.7929600	-0.5661940	-0.9939429	5.930607	6.950892	-0.2840056	-0.6084610	-0.2402939	-0.0577110	-0.7661251	1.562085	1.548762	0.3228087	0.2241062	0.2003846	4.000910	16.248077	0.9428899	0.6683306	0.9333237	0.7310556	0.8815832	0.3113754	1.1250136	0.3783818	0.5250366	-0.0940372	-0.0583917	10.319408	0.4645128	0.0032796	0.0049392	0.0011592	0.000125	0.0002450	0.6248673	0.3332805	1.784737	65.62196	6
4_26-In-006	d1974_1978	2572149	1202965	calibration	poor	1	1	6.900000	6.947128	7.203502	7.700000	127.41681	1.7064546	-10.87862	-11.8201790	6	7	0.0860347	0.0544361	4.975796	1317.000	9931.672	58	98	183	1.568917	1.533827	0.0204794	0.1484705	19.0945148	-0.0190294	0.0021045	0.0221433	0.0000390	3.8783867	4.3162045	2.1377332	-0.0171786	0.1278165	-0.0119618	-0.0501855	-0.3270764	-0.1004921	-0.5133076	2.0736780	2.2502327	-0.3522736	-0.0073879	0.0070676	-0.0522900	-0.3492197	-0.1005311	-0.4981292	2.1899190	2.4300070	-2.4900069	0.0097907	0.4014700	0.7360508	0.5683194	0.8753148	-0.3505180	0.3406741	5.984921	6.984581	-0.5732749	0.4801802	0.4917848	-0.4550385	0.7722272	1.543384	1.558683	0.2730940	0.2489859	0.2376962	4.001326	3.357315	1.0895698	0.9857153	1.0231543	1.0398037	1.0152543	0.5357812	1.3587183	0.0645478	0.5793087	-0.0014692	0.0180000	12.603136	0.5536283	0.0070509	0.0067992	0.0139006	0.000300	0.0005389	0.7573612	0.3395441	1.832904	69.16074	6
4_26-In-012	d1974_1978	2572937	1203693	calibration	moderate	1	1	6.200000	6.147128	5.603502	5.904355	143.41533	0.9372618	22.10210	0.2093917	6	7	0.0737963	3.6830916	4.986864	1315.134	9935.438	58	98	183	1.569093	1.543057	0.0048880	0.1112066	-9.1396294	0.0039732	0.0009509	0.0431735	0.0034232	0.7022317	0.4170935	-0.4178924	-0.0026431	-0.0183221	0.0015183	-0.0079620	0.0053904	-0.0091239	-0.0110896	0.3974485	0.2292406	-0.2168447	-0.0013561	-0.0024548	-0.0089129	-0.0377831	-0.0125471	-0.0052359	0.4158890	0.2700820	0.2010477	0.0012870	0.6717541	0.4404107	-0.6987815	-0.3866692	-0.1960597	-0.7592779	5.953919	6.990917	-0.3006475	-0.9221049	-0.9633239	-0.3257418	-0.9502072	1.565405	1.563151	0.2305476	0.2182523	0.1434273	4.000320	11.330072	0.5758902	0.5300468	0.5107915	0.5744110	0.4975456	0.2001768	0.7160403	0.1311051	0.4620202	0.0340407	-0.0145804	7.100000	0.4850160	0.0021498	0.0017847	0.0011398	0.000000	0.0000124	0.7978453	0.4455501	1.981526	63.57096	6
4_26-In-014	d1974_1978	2573374	1203710	validation	well	0	0	6.600000	6.754607	7.200000	7.151129	165.80418	0.7653937	-20.11569	-7.7729993	6	7	0.0859686	0.0075817	5.285522	1315.160	9939.923	58	98	183	1.569213	1.542792	0.0064054	0.3710849	-0.9318936	-0.0371234	0.0029348	-0.1056513	0.0127788	1.5150748	0.2413423	-0.0289909	0.0020990	-0.0706228	-0.0113604	-0.0301961	-0.0346193	-0.0273140	-0.0343277	0.8245047	0.1029889	-0.0272214	-0.0041158	0.0257630	-0.0331309	0.0710320	-0.0400928	0.0529446	0.8635767	0.1616543	0.0017695	-0.0062147	0.4988544	0.4217250	-0.8485889	-0.8657616	-0.8836724	-0.8993938	4.856076	6.964162	-0.5735765	-0.4998477	-0.4677161	-0.4121092	-0.4782534	1.562499	1.562670	0.3859352	0.2732429	0.1554769	4.000438	42.167496	0.8873205	0.8635756	0.9015982	0.8518201	0.5767300	0.2149791	0.8482135	0.3928713	0.8432562	0.0686932	-0.0085602	8.303085	0.3951114	0.0008454	0.0021042	0.0000000	0.000100	0.0000857	0.4829135	0.4483251	2.113142	64.60535	6
4_26-In-015	d1968_1974_ptf	2573553	1203935	validation	moderate	0	1	6.272715	6.272715	6.718392	7.269008	61.39244	1.0676192	-55.12566	-14.0670462	6	7	0.0650000	0.0007469	5.894688	1315.056	9942.032	58	98	183	1.570359	1.541979	0.0042235	0.3907509	4.2692256	0.0378648	0.0022611	-0.1020419	0.0161510	3.6032522	1.8169731	0.6409346	0.0346340	0.0476020	0.0378154	-0.0179657	-0.0137853	-0.0146946	0.0060875	1.4667766	0.9816071	0.2968794	0.0337645	-0.0000494	-0.0202268	0.0882566	-0.0308456	0.0929077	2.6904552	1.0218329	-0.3440553	-0.0008695	0.6999696	0.3944107	-0.8918364	-0.8864348	-0.7795515	-0.4249992	4.130917	6.945287	0.4304937	0.4614536	0.5919228	0.6559467	0.4574654	1.550528	1.562685	0.4330348	0.3299487	0.1889674	4.000948	5.479310	1.8937486	1.2098556	1.5986075	1.2745584	2.7759163	0.5375320	1.2301254	0.3582314	1.1426100	0.3005829	0.0061576	10.110727	0.5134069	0.0043268	0.0045225	0.0054557	0.000200	0.0002062	0.6290755	0.3974232	2.080674	61.16533	6
4_26-In-016	d1968_1974_ptf	2573310	1204328	calibration	poor	0	1	6.272715	6.160700	5.559031	5.161655	310.05014	0.1321367	-17.16055	-28.0693741	6	7	0.0731646	0.0128017	5.938320	1315.000	9940.597	58	98	183	1.569434	1.541606	0.0040683	0.1931891	-0.1732794	-0.1602274	-0.0035833	-0.1282881	0.0003549	1.5897882	0.8171870	0.0318570	-0.0123340	0.0400775	-0.0813964	-0.0049875	0.0320331	-0.0049053	0.0374298	0.7912259	0.3455668	0.0100844	-0.0059622	0.0788309	-0.0014042	0.1603212	-0.0052602	0.0867119	1.0207798	0.6147888	-0.0217726	0.0063718	0.3157751	0.5292308	-0.8766075	0.5905659	0.8129975	0.1640853	2.030315	6.990967	0.6325440	0.8054439	0.5820994	0.7448481	0.6081498	1.563066	1.552568	0.3688371	0.2607146	0.1763995	4.000725	13.499996	1.0418727	0.8515157	1.2106605	0.8916541	1.2163279	0.4894866	1.0906221	0.2049688	0.7156029	-0.0910767	0.0034276	9.574804	0.3864355	0.0001476	0.0003817	0.0000000	0.000525	0.0001151	0.6997021	0.4278295	2.041467	55.78354	6

Great that worked without problems. Now, to allow spatial trend in more directions than only in a north-south and east-west direction, we have to add polar coordinates:

Code

final_data <- 
  merged_data |> 
    dplyr::mutate(
      x30 = x*cos(30/180*pi) - y*sin(30/180*pi),
      y30 = x*sin(30/180*pi) + y*cos(30/180*pi),
      x60 = x*cos(60/180*pi) - y*sin(60/180*pi),
      y60 = x*sin(60/180*pi) + y*cos(60/180*pi)
    )

Not all our covariates may be continuous variables and therefore have should be encoded as factors. As an easy check, we can take the original corvariates data and check for the number of unique values in each raster. If the variable is continuous, we expect that there are a lot of different values - at maximum 1052 different values because we have that many entries. So, let’s have a look and assume that variables with 10 or less different values are categorical variables.

Code

cat_vars <- 
  covar_data |> 
  # Get number of distinct values per variable
  dplyr::summarise(dplyr::across(dplyr::everything(), ~ dplyr::n_distinct(.))) |> 
  # Turn df into long format for easy filtering
  tidyr::pivot_longer(dplyr::everything(), 
                      names_to = "variable", 
                      values_to = "n") |> 
  # Filter out variables with 10 or less distinct values
  dplyr::filter(n <= 10) |>
  # Extract the names of these variables
  dplyr::pull('variable')

cat("Variables with less than 10 distinct values:", cat_vars)

Variables with less than 10 distinct values: geo500h1id geo500h3id lgm vszone

Now that we have the names of the categorical values, we can mutate these columns in our df using the base function as.factor():

Code

final_data <- 
  final_data |> 
  dplyr::mutate(dplyr::across(cat_vars, ~ as.factor(.)))

2.2 Checking missing data

We are almost done with our data preparation, we just need to reduce it to sampling locations for which we have a decent amount of data on the covariates. Else, we blow up the model calibration with data that is not informative enough.

Code

# Get number of rows to calculate percentages
n_rows <- nrow(final_data)

# Get number of distinct values per variable
final_data |> 
  dplyr::summarise(dplyr::across(dplyr::everything(), 
                                 ~ length(.) - sum(is.na(.)))) |> 
  tidyr::pivot_longer(dplyr::everything(), 
                      names_to = "variable", 
                      values_to = "n") |>
  dplyr::mutate(perc_available = round(n/n_rows *100)) |> 
  dplyr::arrange(perc_available) |> 
  head(10) |> 
  knitr::kable()

variable	n	perc_available
ph.30.50	856	81
ph.10.30	866	82
ph.50.100	859	82
timeset	871	83
ph.0.10	870	83
dclass	1006	96
site_id_unique	1052	100
x	1052	100
y	1052	100
dataset	1052	100

This looks good, we have no variable with a substantial amount of missing data. Generally, only pH measurements are lacking, which we should keep in mind when making predictions and inferences. Another great way to explore your data, is using the {visdat} package:

Code

final_data |> visdat::vis_miss()

Alright, we see that we are not missing a lot of data for any variable and in total only 1% of our data is NA. That is good enough! We do not have to modify the dataframe any further and can save it for further analysis.

2.3 Save data

Code

saveRDS(final_data, 
        here::here("data/bern_sampling_locations_with_covariates.rds"))